Research and Statistical Support

SPSS Short Course

Course Materials Supplemental Materials
Part I: Introduction

Module 1: Basic introduction to SPSS.

Module 2: Graphing.

Module 3: Getting Descriptive Statistics.

Descriptive statistics and the Explore Function

Module 4: Recoding an item.

Recode Sex (from 1 = Female, 2 = Male into 0 = Female, 1 = Male)

Recode Recall 1 using quartiles.

Recode a 5-point Likert response scale if it is reverse coded to begin with

SPSS User Manuals in Adobe.pdf   (Last updated July 12, 2012).

Don't want to pay for SPSS? Then get PSPP for free!! PSPP is extremely similar to SPSS; but free!

Research and Statistical Support statistical resources workshop

Fairly comprehensive comparison of just about all statistical software packages: Wiki

An esteemed former colleague's collection of materials for the courses he taught.

Need to calculate a-priori power/sample size? Check out Gpower (it's free).

Part II: Intermediate Part III: Advanced (some commonly used analyses)

Module 5: Compute (simple)

Create an average of Recall 1 and Recall 2

Use the Compute Function to recode a Likert response scale item

Use Compute to create a total score of multiple variables.

Module 6: Replace Missing Values

Multiple Imputation using a version of the EM algorithm

Module 7: Select cases (create a filter variable).

Select only sophomores in 'ExampleData002.sav'

Module 8: Merge data files.

Restructure data from Long format to Wide format

Module 9: Testing Mean Differences

The t tests and an associated graph.

General comments & 1 regression with scatterplot and 1 oneway ANOVA w/graphs

More detailed examination of ANOVA techniques.

Module 10: Regression

Detailed examination of ordinary least squares (OLS) linear regression (1) (2) (3)

An example of Canonical Correlation

Syntax example of Simple Slopes Analysis -- testing moderation with OLS regression.

Syntax example Testing Mediation w/ Aroian test and OLS regression (w/ this data).

Categorical Regression with Optimal Scaling; and a Better Second Example

Logistic Regression: Binary or Binomial and Multinomial

Module 11: Variable Reduction & Structure

Categorical Principal Components Analysis with optimal scaling

Factor Analysis with Maximum Likelihood extraction

Internal Consistency Analysis

Correspondence Analysis: 2 variable example

Exploration of Linear Mixed Models (i.e. Hierarchical Linear Modeling).

The RSS DIY Introduction to R short course